Object tracking and speed estimation is one of the important and practical tasks in machine vision . Numerous approaches for object tracking and speed estimation have been proposed. So classification and survey of the proposed methods can be very useful especially for new researches. The goal of this paper is to review object tracking methods, classify them into different categories and identify new trends. Then, we propose a method to estimate the speed of moving object from single images based on motion blur analysis. There is direct relation between motion parameters and object speed. Speed estimation is performed by camera parameters, imaging geometry and extracted blur parameters. The radon Transform is used for extracted motion blur parameter. Our proposed method decrease the coast of active methods for speed estimating and improved the measurement of motion blur parameters.

In this paper the induction motor’s parameters and speed are estimated by the application of two methods. The Extended Kalman Filter (EKF) and Recursive Least Square (RLS) are used to implement the estimator. Combination of these algorithms has been...

The accuracy of the object tracking is dependent on the tracking time interval. Smaller tracking time interval increases the accuracy of tracking a moving object. However, this increases the power consumption significantly. This paper proposes two...

In this paper we propose a new method for multi–feature object tracking in a particle filter framework. Each particle indicates one hypothesis of tracked object. In common method of feature combination, each particle measures all features. Due to...

In this paper the methods of speed estimation, widely used in polyphase AC motors, is adapted to single phase induction motors. A machine model in the stationary reference frame is used for the estimation. The method is applied to two single phase...